CN111628515A - Ground type hybrid energy storage system control method for rail transit - Google Patents
Ground type hybrid energy storage system control method for rail transit Download PDFInfo
- Publication number
- CN111628515A CN111628515A CN202010557730.7A CN202010557730A CN111628515A CN 111628515 A CN111628515 A CN 111628515A CN 202010557730 A CN202010557730 A CN 202010557730A CN 111628515 A CN111628515 A CN 111628515A
- Authority
- CN
- China
- Prior art keywords
- energy storage
- model
- sub
- cha
- dis
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/34—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering
- H02J7/345—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering using capacitors as storage or buffering devices
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a ground type hybrid energy storage system control method for rail transit, which comprises the steps of establishing a system model, and identifying the system model by adopting a total measurement and identification method, namely an identification method according to input and output data; in the estimation of system model parameters, a charging threshold value U is usedchaDischarge threshold UdisAs system input variable, the output energy E of the substationsubAs an output variable; and identifying the traction power supply system model in different time periods, performing online correction, dynamically adjusting the charge-discharge threshold of the ground hybrid energy storage device online, and improving the energy saving rate of the energy storage system.
Description
Technical Field
The invention belongs to the technical field of utilization of regenerative braking energy of urban rail transit, and particularly relates to a control method of a ground type hybrid energy storage system for rail transit.
Background
At present, in urban rail transit in China, a plurality of lines are provided with ground-based super capacitors to store braking energy. Due to the fact that the energy density of the super capacitor is low, enough emergency self-traction energy cannot be provided for the power supply failure train. The hybrid energy storage system of the battery and the super capacitor is mainly applied to the fields of wind power, micro-grids, electric vehicles and the like. The urban rail transit is different from the above application scenarios, for example, the existing operation diagram of a train is relatively fixed during operation, the departure interval of the train changes with time, and the no-load voltage of a transformer substation fluctuates, which all affect the energy saving effect of the energy storage system. The battery and super capacitor hybrid energy storage system has no example of application to urban rail transit.
A block diagram of a conventional relatively typical ground-based hybrid energy storage system control method is shown in fig. 1.
The principle is that the charging and discharging of the energy storage device are directly controlled according to the charging and discharging threshold value instruction and the deviation value of the actual network voltage. The method can be divided into three parts: voltage outer loop controller, power distribution, current inner loop controller. The voltage outer loop controller detects the direct current network voltage as feedback, and obtains a charge-discharge power instruction value of the energy storage device through a PI regulator according to the difference value between the direct current network voltage and a charge threshold or a discharge threshold; then distributing the charge and discharge power borne by the battery and the super capacitor, and obtaining charge and discharge current instruction values of the battery and the super capacitor; and the current inner loop controller respectively detects the charging and discharging currents of the battery and the super capacitor as feedback, and obtains control pulses of the upper and lower tubes of the bidirectional DC/DC converter of the energy storage system through a PI regulator according to the difference value between the feedback and the current instruction value, so that the energy storage system is controlled.
This control strategy is relatively simple, but has drawbacks. The control strategy sets a fixed charging and discharging threshold value instruction, which is easily influenced by the no-load voltage of a transformer substation, the specific braking of a train and the change of the departure interval of the train, so that the start of a vehicle-mounted braking resistor is caused, and the energy-saving effect is poor; a charging control strategy considering network voltage fluctuation is also provided, the basic control principle of the method is to control the terminal voltage of the energy storage device to closely follow the fluctuation of the network voltage of the dynamic traction network, so that the residual regenerative braking energy of the train is absorbed by the energy storage device as much as possible, and the method needs to obtain the position of the train in real time and is difficult to realize in practical application.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the defects in the prior art, the invention aims to improve the energy-saving effect of a system, reduce the output energy of a transformer substation, fully utilize an energy storage device, and provide a charging and discharging threshold value adjusting strategy based on model online correction.
The technical scheme is as follows: in order to achieve the purpose of the invention, the technical scheme adopted by the invention is as follows:
the control method of the ground type hybrid energy storage system for rail transit comprises the steps of establishing a system model, and identifying the system model by adopting a total measurement and identification method, namely an identification method according to input and output data; in the estimation of system model parameters, a charging threshold value U is usedchaDischarge threshold UdisAs system input variable, the output energy E of the substationsubAs an output variable;
the method comprises the following specific steps:
s01: measuring desired data, including no-load voltage U of substation0And the transformer substation outputs the lowest voltage U when the train operatesminTrain brake resistance starting voltage UR；
S02: determining system input variable regionsAt intervals, i.e. with departure intervals T in the up-and-down line of the trainsAs a measurement period, a measurement interval with Δ U as a system input variable;
s03: acquisition of system model identification parameters, i.e. measurement at different charging thresholds UchaDischarge threshold UdisCorresponding transformer substation output energy E under the action ofsub；
S04: fitting the system model parameters to obtain an estimated value of the system model parameters; establishing a new system model; namely obtaining the output energy E of the transformer substation in different periodssubAnd a charging threshold UchaDischarge threshold UdisA mathematical model in between; the charging and discharging control problem of the hybrid energy storage system can be converted into an optimization method;
s05: optimizing the new system model and simultaneously performing online correction; wherein the optimization method is to predict the next train operation period T by using the method of solving the extreme valuesEnergy storage device charging threshold U for minimizing output energy of transformer substationchaDischarge threshold UdisAnd substation output energy Esub,m(ii) a Setting the predicted value as a charging and discharging threshold value of the energy storage device; next train operation period TsAfter the period is finished, the output energy E of the actual transformer substation in the period is measuredsubAnd the predicted value Esub,mBy contrast, the difference Δ E ═ E is calculatedsub-Esub,mCarrying out online correction on the parameter estimation value of the original system prediction model in the current time period when the delta E meets the condition; and predicting the next period after the corrected model is obtained, so as to form a cycle.
Further, the system model adopts a polynomial model or a power function model in a static model or a combined model structure of the two models.
Further, the system model adopts a polynomial model,
further, the system model is divided into a low peak period, a flat peak period and a high peak period, and the three periods are independently identified.
Further, the measurement formula in step S03 is:
further, in step S04, the fitting function adopts a least squares method.
Further, in step S04, the mathematical model of the new system model is:
Esub＝f1(Ucha,Udis)。
further, in step S05, the optimization method, i.e. the manner of finding the extremum, is as follows:
Minimize Esub
s.t.Ucha,min≤Ucha≤Ucha,max。
Udis,min≤Udis≤Udis,max
further, in step S05, an online correction is performed by using a recursive least square method.
Has the advantages that: compared with the prior art, the invention has the following advantages: and identifying the traction power supply system model in different time periods, performing online correction, dynamically adjusting the charge-discharge threshold of the ground hybrid energy storage device online, and improving the energy saving rate of the energy storage system.
Drawings
FIG. 1 is a block diagram of a typical ground-based hybrid energy storage system control method in the background art;
FIG. 2 is a diagram of an urban rail transit power supply system of the hybrid energy storage system of the present invention;
FIG. 3 is a model of a traction power supply system;
FIG. 4 is a schematic block diagram of online correction of a prediction model;
FIG. 5 is a flow chart of the technical solution in the example.
Detailed Description
The present invention will be further illustrated with reference to the accompanying drawings and specific examples, which are carried out on the premise of the technical solution of the present invention, and it should be understood that these examples are only for illustrating the present invention and are not intended to limit the scope of the present invention.
As shown in fig. 3, the energy flow of the traction power supply system directly affects the output energy of the substation, and the energy flow of the traction power supply system is related to many factors, including train operation curves, train departure intervals, no-load voltage of the substation, charge and discharge thresholds of the energy storage device, and the like, which cause complexity of the traction power supply system model. Among the factors that influence the flow of energy, most are not controllable, and the only factor that can be controlled is the charge-discharge threshold of the energy storage device. If a mathematical model of the traction power supply system can be obtained, the charge and discharge threshold value of the energy storage device with the minimized output energy of the time-varying power station under different conditions can be obtained.
Therefore, the method adopts a total measurement and identification method, namely, a method for identifying the model according to input and output data, and the charging threshold value U is used for model identification during the parameter estimation of the system modelchaDischarge threshold UdisAs system input variable, the output energy E of the substationsubAs an output variable.
And establishing a system model 1 for describing the rule of the change of the output energy of the transformer substation in a period of time along with the running of the train, and selecting a proper mathematical model according to the actual measurement data. The method adopts a polynomial model or a power function model or a combined model structure of two models commonly used by a static model, and the formula is a polynomial model as shown in formula (1).
Because the train departure interval changes along with the time and is fixed and unchanged in a period of time, the system model is divided into three periods of low peak time, flat peak time and high peak time according to the train departure interval for independent identification.
When estimating the parameters of the system model, all the measurement data including the no-load voltage U of the transformer substation are needed0And the transformer substation outputs the lowest voltage U when the train operatesminTrain brake resistance starting voltage UR(ii) a Then determining the departure interval T between the input variable interval of the system and the up-down line of the trainsAs a measurement period, measuring at different charging thresholds U with Δ U as a measurement interval of the system input variablechaDischarge threshold UdisCorresponding transformer substation output energy E under the action ofsub。
In the actual processing process, the measured data is usually given step by step according to the time sequence, and we can process a batch of data already obtained, as shown in formulas (2) and (3),
and during calculation, the time sequence among the measurement data is not considered, and then the least square method is adopted to fit the system model parameters according to the measurement data to obtain the estimated values of the system model parameters.
Further obtaining the output energy E of the transformer substation in different periodssubAnd a charging threshold UchaDischarge threshold UdisThe new mathematical model 2 in between, i.e. the mathematical analytical expression, is shown in equation (4).
Esub＝f1(Ucha,Udis) (4)
The charge-discharge control problem of the hybrid energy storage system can be converted into an optimization method as shown in the formula (5),
Minimize Esub
s.t.Ucha,min≤Ucha≤Ucha,max(5)
Udis,min≤Udis≤Udis,max
the system model recognized according to the current time interval, namely formula (4) predicts the next train operation by an extremum solving method under the constraint condition of formula (5)Line period TsEnergy storage device charging threshold U for minimizing output energy of transformer substationchaDischarge threshold UdisAnd substation output energy Esub,m. And setting the predicted value as a charging and discharging threshold value of the energy storage device.
After the next train operation period is finished, the actual substation output energy E of the period is measuredsubAnd the predicted value Esub,mBy contrast, the difference Δ E ═ E is calculatedsub-Esub,mCarrying out online correction on the parameter estimation value of the original system prediction model in the current time period when the delta E meets the condition; and predicting the next period after the corrected model is obtained, so as to form a cycle. As shown in fig. 4, fig. 4 is a schematic block diagram of online correction of a prediction model.
According to the method, online correction of system modeling is performed by using a recursive least square method, the method is small in calculated amount and high in calculating speed, and real-time online application can be achieved. After the corrected model is obtained, the charging threshold U of the energy storage device of the next train running period is predictedchaDischarge threshold UdisAnd substation output energy Esub,m. Through online correction of the traction power supply system model and dynamic adjustment of the charging and discharging threshold of the energy storage device, the minimum output energy of the transformer substation is realized, and the energy-saving effect of the system is improved.
The following takes the data of the peak time of eight-way line of Beijing subway as an example, and the process of the invention is described in detail in the same way in other time periods.
As shown in fig. 5, fig. 5 is a flowchart of the technical solution in this embodiment.
1. According to the train departure schedule, the system model is divided into three periods of low peak time, flat peak time and high peak time to be independently identified. The time interval with the shortest departure interval is the peak time, the time interval with the longest departure interval is the low peak time, and the time interval with the moderate departure interval is the flat peak time.
2. Measuring the no-load voltage U of the transformer substation in the period0830V, the lowest voltage U is output by the transformer substation when the train operatesmin750V train brake resistor starting voltage URDetermining a system input variable charging threshold U at 900VchaDischarge threshold UdisZone (D) ofAnd (3) removing the solvent. At a measurement interval with Δ U as the input variable of the system (Δ U ═ 10V), there is Ucha＝[U0+ΔU，U0+2ΔU，U0+3ΔU，…，UR]，Udis＝[Umin，U0-ΔU]。
3. Departure interval T in up and down lines of train in this time periodsSetting different charging thresholds U of the hybrid energy storage system as a measurement period of 180schaAnd a discharge threshold UdisThe combination is obtained, the output voltage and the current of the transformer substation are measured in real time, and the output energy E of the transformer substation is obtained by calculating the time intervalsubAs shown in formulas (6) and (7).
4. And fitting the system model by adopting a least square method according to the measured data to obtain an estimated value of the system model parameter.
Further obtaining the output energy E of the transformer substation in different periodssubAnd a charging threshold UchaDischarge threshold UdisIn between the new mathematical model 2 of the model,
5. based on the system mathematical model obtained by fitting and the limit of the charge/discharge threshold, equation (5) can be rewritten as equation (9). Calculating the equation E under the current model by using a mathematical method for calculating an extreme valuesubMinimum charging threshold UchaDischarge threshold UdisAnd substation output energy Esub,m. Charging threshold U to be calculatedchaDischarge threshold UdisSet as the charge-discharge threshold of the energy storage device. The departure interval T of the next trainsAfter the period is finished, the output energy E of the actual transformer substation in the period is measuredsubAnd the predicted value Esub,mBy contrast, the difference Δ E ═ E is calculatedsub-Esub,mIf Δ E>And (5) performing online correction on the parameter estimation value of the original system prediction model in the current time period by adopting a recursive least square method, wherein the parameter estimation value is 1 kwh.And then performing re-prediction according to the modified model.
Minimize Esub
s.t.840≤Ucha≤900 (9)
750≤Udis≤820
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (9)
1. The ground type hybrid energy storage system control method for rail transit is characterized by comprising the following steps: establishing a system model, and identifying the system model by adopting a total measurement and identification method, namely an identification method according to input and output data; in the estimation of system model parameters, a charging threshold value U is usedchaDischarge threshold UdisAs system input variable, the output energy E of the substationsubAs an output variable; the method comprises the following specific steps:
s01: measuring desired data, including no-load voltage U of substation0And the transformer substation outputs the lowest voltage U when the train operatesminTrain brake resistance starting voltage UR；
S02: determining system input variable interval, i.e. departure interval T in up-down line of trainsAs a measurement period, a measurement interval with Δ U as a system input variable;
s03: acquisition of system model identification parameters, i.e. measurement at different charging thresholds UchaDischarge threshold UdisCorresponding transformer substation output energy E under the action ofsub；
S04: fitting the system model parameters to obtain an estimated value of the system model parameters; establishing a new system model; namely obtaining the output energy E of the transformer substation in different periodssubAnd a charging threshold UchaDischarge threshold UdisA mathematical model in between; the charging and discharging control problem of the hybrid energy storage system can be converted into an optimization method;
s05: optimizing the new system model and simultaneously performing online correction; wherein the optimization method is to predict the next train operation period T by using the method of solving the extreme valuesEnergy storage device charging threshold U for minimizing output energy of transformer substationchaDischarge threshold UdisAnd substation output energy Esub,m(ii) a Setting the predicted value as a charging and discharging threshold value of the energy storage device; next train operation period TsAfter the period is finished, the output energy E of the actual transformer substation in the period is measuredsubAnd the predicted value Esub,mBy contrast, the difference Δ E ═ E is calculatedsub-Esub,mCarrying out online correction on the parameter estimation value of the original system prediction model in the current time period when the delta E meets the condition; and predicting the next period after the corrected model is obtained, so as to form a cycle.
2. The ground-based hybrid energy storage system control method for rail transit of claim 1, characterized in that: the system model adopts a polynomial model or a power function model in a static model or a combined model structure of the two models.
3. The ground-based hybrid energy storage system control method for rail transit of claim 2, characterized in that: the system model 1 is a polynomial model,
4. the ground-based hybrid energy storage system control method for rail transit of claim 1, characterized in that: the system model is divided into three periods of a low peak period, a flat peak period and a high peak period for independent identification.
5. The ground-based hybrid energy storage system control method for rail transit of claim 1, characterized in that: the measurement formula in step S03 is:
6. the ground-based hybrid energy storage system control method for rail transit of claim 1, characterized in that: in step S04, the fitting function uses the least square method.
7. The ground-based hybrid energy storage system control method for rail transit of claim 1, characterized in that: in step S04, the mathematical model of the new system model is:
Esub＝f1(Ucha,Udis)。
8. the ground-based hybrid energy storage system control method for rail transit of claim 1, characterized in that: in step S05, the extremum is obtained by:
Minimize Esub
s.t.Ucha,min≤Ucha≤Ucha,max
Udis,min≤Udis≤Udis,ma。x
9. the ground-based hybrid energy storage system control method for rail transit of claim 1, characterized in that: in step S05, a recursive least square method is used for online correction.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010557730.7A CN111628515B (en) | 2020-06-18 | 2020-06-18 | Ground type hybrid energy storage system control method for rail transit |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010557730.7A CN111628515B (en) | 2020-06-18 | 2020-06-18 | Ground type hybrid energy storage system control method for rail transit |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111628515A true CN111628515A (en) | 2020-09-04 |
| CN111628515B CN111628515B (en) | 2021-08-27 |
Family
ID=72271239
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010557730.7A Active CN111628515B (en) | 2020-06-18 | 2020-06-18 | Ground type hybrid energy storage system control method for rail transit |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111628515B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2016106373A1 (en) * | 2014-12-22 | 2016-06-30 | Robert Bosch Gmbh | Method for adaptive demand charge reduction |
| CN107069782A (en) * | 2016-11-29 | 2017-08-18 | 北京交通大学 | Capacity collocation method applied to rail transportation vehicle-mounted mixed energy storage system |
| CN107895960A (en) * | 2017-11-01 | 2018-04-10 | 北京交通大学长三角研究院 | City rail traffic ground type super capacitor energy storage system energy management method based on intensified learning |
| CN109672200A (en) * | 2018-12-11 | 2019-04-23 | 北京交通大学 | City rail traffic ground super capacitor energy-storage system distributed and coordinated control optimization method |
| CN109980669A (en) * | 2019-04-11 | 2019-07-05 | 湖南工业大学 | The urban rail super capacitor energy-storage system control method for being set and being coordinated and optimized based on dynamic |
| CN110752654A (en) * | 2019-09-27 | 2020-02-04 | 南京工程学院 | Energy scheduling method for tramcar hybrid energy storage system |
-
2020
- 2020-06-18 CN CN202010557730.7A patent/CN111628515B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2016106373A1 (en) * | 2014-12-22 | 2016-06-30 | Robert Bosch Gmbh | Method for adaptive demand charge reduction |
| CN107069782A (en) * | 2016-11-29 | 2017-08-18 | 北京交通大学 | Capacity collocation method applied to rail transportation vehicle-mounted mixed energy storage system |
| CN107895960A (en) * | 2017-11-01 | 2018-04-10 | 北京交通大学长三角研究院 | City rail traffic ground type super capacitor energy storage system energy management method based on intensified learning |
| CN109672200A (en) * | 2018-12-11 | 2019-04-23 | 北京交通大学 | City rail traffic ground super capacitor energy-storage system distributed and coordinated control optimization method |
| CN109980669A (en) * | 2019-04-11 | 2019-07-05 | 湖南工业大学 | The urban rail super capacitor energy-storage system control method for being set and being coordinated and optimized based on dynamic |
| CN110752654A (en) * | 2019-09-27 | 2020-02-04 | 南京工程学院 | Energy scheduling method for tramcar hybrid energy storage system |
Non-Patent Citations (1)
| Title |
|---|
| FEIQIN ZHU; ZHONGPING YANG: "Dynamic Threshold Adjustment Strategy of Supercapacitor Energy Storage System based on No-load Voltage Identification in Urban Rail Transit", 《 2019 IEEE TRANSPORTATION ELECTRIFICATION CONFERENCE AND EXPO, ASIA-PACIFIC (ITEC ASIA-PACIFIC)》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111628515B (en) | 2021-08-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110053521B (en) | Urban rail transit traction power supply system and vehicle-network coordination parameter optimization method | |
| CN111422094B (en) | Charging and discharging coordination optimization control method for distributed charging pile | |
| US20120274268A1 (en) | Power Monitoring and Control Apparatus and Power Monitoring and Control System | |
| CN103048626A (en) | Method for accurately estimating SOC (State of Charge) | |
| CN110549914B (en) | Approximate optimal energy management method for daily operation of fuel cell tramcar | |
| CN110341508B (en) | Dynamic wireless charging load prediction control method for electric automobile | |
| CN106340892B (en) | For stabilizing the control equipment of the energy-storage system of wind power output power | |
| CN112909937B (en) | Multi-mode digital twinning simulation method and device for rail transit traction power supply system | |
| CN111398828A (en) | Method for estimating remaining electric quantity and remaining driving mileage of electric automobile | |
| CN109378881A (en) | A kind of power battery pack bidirectional self-adaptive balance control method | |
| CN106208031B (en) | It can inhibit the improvement droop control method and system of circulation in direct-current micro-grid | |
| CN113131490B (en) | Reactive power control method, device and system for new energy station | |
| CN109383323A (en) | A kind of charge and discharge optimal control method of electric car group | |
| AU2021394197A1 (en) | Control system, reactive voltage control method and device, medium, and calculation device | |
| CN111628515B (en) | Ground type hybrid energy storage system control method for rail transit | |
| CN116316755B (en) | Energy management method for electrified railway energy storage system based on reinforcement learning | |
| CN116404671B (en) | Multi-energy-storage state-of-charge equalization strategy of direct-current micro-grid based on layered cooperative control | |
| CN110829474B (en) | Method and system for supporting dynamic security of power grid by using big data intelligent energy storage | |
| CN112217226B (en) | Improved model-free predictive control method suitable for bidirectional DC-DC converter | |
| CN107634542B (en) | Grid-connected power smooth control method and controller of new energy power generation system | |
| CN114513017B (en) | Distributed tracking method and system for power distribution network instructions of alternating current-direct current micro-grid | |
| Pavković et al. | Current and voltage control system designs with EKF-based state-of-charge estimator for the purpose of LiFePO4 battery cell charging | |
| CN113085561B (en) | Locomotive regenerative braking energy distribution method, system and device | |
| CN110445130B (en) | Static voltage stability margin calculation device considering optimal reactive power support | |
| CN112557813B (en) | Method for judging voltage stability of power grid under simultaneous fault of multiple loops of direct current |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |